RRepoGEO

REPOGEO REPORT · LITE

ashvardanian/NumKong

Default branch main · commit 63a254f4 · scanned 6/25/2026, 2:17:12 PM

GitHub: 1,835 stars · 124 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ashvardanian/NumKong, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify README H1 and first sentence to explicitly state core identity and disassociate from misinterpretations

    Why:

    CURRENT
    # NumKong: Mixed Precision for All Portable mixed-precision math, linear-algebra, & retrieval library...
    COPY-PASTE FIX
    # NumKong: Universal SIMD-Accelerated BLAS-like Library for Mixed-Precision Math. NumKong is a portable, high-performance library for mixed-precision linear algebra, vector operations, and information retrieval, offering 2'000+ SIMD kernels across x86, Arm, RISC-V, and WebAssembly, with bindings for C++, Python, Rust, JavaScript, Go, and Swift. (Note: This project is not affiliated with Kong Gateway or microservices solutions.)
  • mediumtopics#2
    Add more specific topics related to high-performance numerical computing and linear algebra

    Why:

    CURRENT
    arm-neon, assembly, blas, cpp, golang, information-retrieval, javascript, matrix-multiplication, metrics, numpy, rust, scipy, simd, swift, tensor, vector-search
    COPY-PASTE FIX
    arm-neon, assembly, blas, cpp, golang, high-performance-computing, information-retrieval, javascript, linear-algebra, matrix-multiplication, metrics, numerical-computing, numpy, rust, scipy, simd, swift, tensor, vector-operations, vector-search
  • lowcomparison#3
    Create a dedicated 'Comparison' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Other BLAS-like Libraries
NumKong differentiates itself from libraries like OpenBLAS, Intel MKL, BLIS, and Eigen by offering a unique combination of broad mixed-precision support, extensive architecture and language bindings, robust numerical stability through wider accumulators, and a significantly smaller binary footprint. It aims to provide a modern, portable alternative for high-performance numerical computing.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface ashvardanian/NumKong
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenBLAS
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenBLAS · recommended 1×
  2. Intel oneAPI Math Kernel Library (oneMKL) · recommended 1×
  3. BLIS (Basic Linear Algebra Subprograms) · recommended 1×
  4. Arm Performance Libraries · recommended 1×
  5. Eigen · recommended 1×
  • CATEGORY QUERY
    How to perform fast SIMD-accelerated linear algebra operations across multiple CPU architectures?
    you: not recommended
    AI recommended (in order):
    1. OpenBLAS
    2. Intel oneAPI Math Kernel Library (oneMKL)
    3. BLIS (Basic Linear Algebra Subprograms)
    4. Arm Performance Libraries
    5. Eigen

    AI recommended 5 alternatives but never named ashvardanian/NumKong. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Library for mixed-precision vector operations with robust numerical stability for information retrieval?
    you: not recommended
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of ashvardanian/NumKong?
    pass
    AI named ashvardanian/NumKong explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ashvardanian/NumKong in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ashvardanian/NumKong explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo ashvardanian/NumKong solve, and who is the primary audience?
    pass
    AI named ashvardanian/NumKong explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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ashvardanian/NumKong — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite